• The Journal of Industrial Distribution & Business
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• v.12 no.8
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• pp.7-19
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• 2021

Purpose: This paper aims to investigate (1) whether the interviewed auditors conduct higher quality audit than the non-interviewed auditors and (2) whether the frequency of audit engagement interviews has an impact on audit quality. Research design, data, and methodology: Using a sample of Chinese A-share listed firms between 2011 and 2019, this paper empirically tests the effect of audit engagement interviews on auditor's behavior. We collect the data of audit engagement interviews on the CICPA's website. We use OLS regression, fixed-effect model and random-effect model to examine the association between audit engagement interviews and audit quality. Results: Findings indicate that the audit quality of the interviewed auditors is significantly greater than that of the non-interviewed auditors. The frequency of the audit engagement interviews is positively associated with audit quality. The interviewed auditors spend significantly more time on the audit. Furthermore, the positive association between audit engagement interviews and audit quality only exists in non-Big 4 auditors. Conclusions: Our findings provide evidence for the effectiveness of audit regulation enforcement. The results suggest that in an emerging market with weak legal systems, preventive regulations such as audit interviews have a deterrent effect and are necessary in alleviating information asymmetry and improving information environment.

• The Journal of Asian Finance, Economics and Business
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• v.8 no.4
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• pp.249-261
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• 2021

To tranquilize the devastating impact of unnecessary risk-taking behavior of banks towards the economy for maximizing their profits that usually arises due to widely known 'moral-hazard' problem originating from market competition and intensified by bank's limited liability, the banking system is strongly monitored across all countries of the world. The goal of controlling would become more feasible if there exist some self-discipline and motivations which could safeguard the banks' charter value through the mechanism of market discipline. Therefore, our study is aimed to scrutinize the relation between market discipline and charter value of local commercial banks that are registered on the Pakistan Stock Exchange by analyzing a balanced panel data from the year 2007 to 2019. Deposit growth, interbank deposits, and subordinate debt are taken as proxies to measure market discipline whereas Tobin's Q theory is applied for calculating the charter value. Generalized Least Square Regression with Fixed Effect Model is used for evaluation. The outcomes reveal that in the existence of control variables, all proxies of market discipline have a significant positive impact on bank charter value. Our research has important policy implications for monitoring and supervising financial intermediaries for their stability and soundness by offsetting the complications of moral-hazard in the financial systems.

• Asian Journal of Business Environment
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• v.12 no.4
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• pp.35-44
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• 2022

Purpose: The main purpose of this study is to find out the impact of ownership structure on firm performance in the pharmaceutical and chemical industry of Bangladesh. Research design, data and methodology: The study has been conducted on 28 listed pharmaceutical and chemical companies from 2012 to 2020. Return on Assets (ROA) and Tobin's Q are selected as indicators of internal and market performance of the firms respectively whereas institutional ownership, directors' ownership and foreign ownership are selected as proxies of ownership structure. Panel analysis using random effects, lag method and time dummy method is used to analyse the relationship. Results: The study has found the existence of highly concentrated directors' ownership, a low percentage of institutional ownership and a very insignificant proportion of foreign ownership in the industry. The regression results show that directors' ownership has a positive and significant impact on firm performance, supporting the concept of agency theory. The study has also found a positive and significant impact of foreign ownership on firm performance. Unfortunately, the impact of institutional ownership is found to be insignificant. Conclusions: Directors' ownership and foreign ownership decreases agency cost that ultimately increases firm performance. However, the role of institutional investors is not significant enough to improve firm performance. It is suggested that institutional investors should be more active and involved in monitoring the activities of the organisations to improve performance.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.45 no.1
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• pp.53-70
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• 2022

Recently, due to the change to SASB(Sustainability Accounting Standards Board) and GRI(Global Reporting Initiative) Standards 2021, the paradigm for non-financial information disclosure is changing significantly, with the number of ESG topics and indicators that must be disclosed by industry from an autonomous material topic selection method. This study revealed that the number of compulsory topics in the oil and gas industry by GRI standards 2021 is up to 2.4 times higher than the average number of material topics disclosed when domestic companies publish sustainability reports using GRI Standards 2020. In the oil and gas industry, I analyzed the similarities and differences between the GRI standards 2021 and the ESG topics covered by SASB by environmental, social, economic, and governance areas. In addition, the materiality test process, which is different in GRI standards 2021, is introduced, and the issues included in the following 10 representative ESG-related initiatives are summarized into 62 and suggested improvement plans for materiality test used in the topic pool.

• The Journal of Asian Finance, Economics and Business
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• v.9 no.5
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• pp.225-237
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• 2022

The term "artificial intelligence" is considered a component of sophisticated technological developments, and several intelligent tools have been developed to assist organizations and entrepreneurs in making business decisions. Artificial intelligence (AI) is defined as the concept of transforming inanimate objects into intelligent beings that can reason in the same way that humans do. Computer systems can imitate a variety of human intelligence activities, including learning, reasoning, problem-solving, speech recognition, and planning. This study's objective is to provide responses to the questions: Which factors should be taken into account while deciding whether or not to use AI applications? What role do these elements have in AI application adoption? However, this study proposes a framework to explore the significance and relation of success factors to AI adoption based on the technology-organization-environment model. Ten critical factors related to AI adoption are identified. The framework is empirically tested with data collected by mail surveying organizations in Vietnam. Structural Equation Modeling is applied to analyze the data. The results indicate that Technical compatibility, Relative advantage, Technical complexity, Technical capability, Managerial capability, Organizational readiness, Government involvement, Market uncertainty, and Vendor partnership are significantly related to AI applications adoption.

• Geomechanics and Engineering
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• v.34 no.4
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• pp.341-380
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• 2023

This study evaluates the seismic fragilities in fill slopes in South Korea through parametric finite element analyses that have been barely investigated thus far. We consider three slope geometries for a slope of height 10 m and three slope angles, and two soil types, namely frictional and frictionless, associated with two soil states, loose and dense for frictional soils and soft and stiff for frictionless soils. The input ground motions accounting for four site conditions in South Korea are obtained from one-dimensional site response analyses. By comparing the numerical modeling of slopes using PLAXIS^2D against the previous studies, we compiled suites of the maximum permanent slope displacement (D_max) against two ground motion parameters, namely, peak ground acceleration (PGA) and Arias Intensity (I_A). A probabilistic seismic demand model is adopted to compute the probabilities of exceeding three limit states (minor, moderate, and extensive). We propose multiple seismic fragility curves as functions of a single ground motion parameter and numerous seismic fragility surfaces as functions of two ground motion parameters. The results show that soil type, slope angle, and input ground motion influence these probabilities, and are expected to help regional authorities and engineers assess the seismic fragility of fill slopes in the road systems in South Korea.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.47 no.1
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• pp.20-27
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• 2024

Determining the number of operators who set up the machines in a human-machine system is crucial for maximizing the benefits of automated production machines. A man-machine chart is an effective tool for identifying bottlenecks, improving process efficiency, and determining the optimal number of machines per operator. However, traditional man-machine charts are lacking in accounting for idle times, such as interruptions caused by other material handling equipment. We present an adjusted man-machine chart that determines the number of machines per operator, incorporating idleness as a penalty term. The adjusted man-machine chart efficiently deploys and schedules operators for the hole machining process to enhance productivity, where operators have various idle times, such as break times and waiting times by forklifts or trailers. Further, we conduct a simulation validation of traditional and proposed charts under various operational environments of operators' fixed and flexible break times. The simulation results indicate that the adjusted man-machine chart is better suited for real-world work environments and significantly improves productivity.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.18 no.1
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• pp.1-14
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• 2024

Time-series forecasting is extensively used in the actual world. Recent research has shown that Transformers with a self-attention mechanism at their core exhibit better performance when dealing with such problems. However, most of the existing Transformer models used for time series prediction use the traditional encoder-decoder architecture, which is complex and leads to low model processing efficiency, thus limiting the ability to mine deep time dependencies by increasing model depth. Secondly, the secondary computational complexity of the self-attention mechanism also increases computational overhead and reduces processing efficiency. To address these issues, the paper designs an efficient multi-layer attention-based time-series forecasting model. This model has the following characteristics: (i) It abandons the traditional encoder-decoder based Transformer architecture and constructs a time series prediction model based on multi-layer attention mechanism, improving the model's ability to mine deep time dependencies. (ii) A cross attention module based on cross attention mechanism was designed to enhance information exchange between historical and predictive sequences. (iii) Applying a recently proposed sparse attention mechanism to our model reduces computational overhead and improves processing efficiency. Experiments on multiple datasets have shown that our model can significantly increase the performance of current advanced Transformer methods in time series forecasting, including LogTrans, Reformer, and Informer.

• Journal of Wind Energy
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• v.15 no.2
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• pp.10-22
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• 2024

This research is a comprehensive analysis of wind power prediction sensitivity using a Long Short-Term Memory (LSTM) deep learning neural network model, accounting for the inherent uncertainties in wind speed estimation. Utilizing a year's worth of operational data from an operational wind farm, the study forecasts the power output of both individual wind turbines and the farm collectively. Predictions were made daily at intervals of 10 minutes and 1 hour over a span of three months. The model's forecast accuracy was evaluated by comparing the root mean square error (RMSE), normalized RMSE (NRMSE), and correlation coefficients with actual power output data. Moreover, the research investigated how inaccuracies in wind speed inputs affect the power prediction sensitivity of the model. By simulating wind speed errors within a normal distribution range of 1% to 15%, the study analyzed their influence on the accuracy of power predictions. This investigation provided insights into the required wind speed prediction error rate to achieve an 8% power prediction error threshold, meeting the incentive standards for forecasting systems in renewable energy generation.

• Atmosphere
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• v.34 no.3
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• pp.257-271
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• 2024

In 2023, the World Meteorological Organization released a report on climate conditions in Asia, highlighting the region's high vulnerability to floods and the increasing severity and frequency of extreme precipitation events. While previous studies have largely concentrated on broader-scale phenomena such as the Asian monsoon, it is crucial to investigate the substantial characteristics of extreme precipitation for a better understanding. In this study, we analyze the spatiotemporal characteristics of extreme precipitation during summer and their affecting factors by decomposing the moisture budgets within specific Asian regions over 44 years (1979~2022). Our findings indicate that dynamic convergence terms (DY CON), which reflect changes in wind patterns, primarily drive extreme rainfall across much of Asia. In southern Asian sub-regions, particularly coastal areas, extreme precipitation is primarily driven by low-pressure systems, with DY CON accounting for 70% of the variance. However, in eastern Asia, both thermodynamic advection and nonlinear convergence terms significantly contribute to extreme precipitation. Notably, on the Korean Peninsula, thermodynamic advection plays an important role, driven by substantial moisture carried by strong southerly mean flow. Understanding these distinct characteristics of extreme rainfall across sub-regions is expected to enhance both predictability and resilience.